LMSupply.Embedder

LMSupply.Embedder

Local text embedding for .NET with automatic model downloading.

Features

• Zero-config: Models download automatically from HuggingFace
• GPU Acceleration: CUDA, DirectML (Windows), CoreML (macOS)
• Cross-platform: Windows, Linux, macOS
• Simple API: Just 2 lines of code to get started

Quick Start

using LMSupply.Embedder;

// Load the default model
await using var model = await LocalEmbedder.LoadAsync("default");

// Generate embeddings
float[] embedding = await model.EmbedAsync("Hello, world!");
Console.WriteLine($"Dimensions: {embedding.Length}");

Query/Passage Embeddings

Some models (the E5 family, Nomic) are fine-tuned with an asymmetric text-prefix convention — query embeddings and document/passage embeddings need different prefixes for accurate retrieval. EmbedQueryAsync/EmbedPassageAsync apply the right prefix automatically from the model's ModelInfo (a no-op passthrough for models that don't need one):

await using var model = await LocalEmbedder.LoadAsync("multilingual-e5-base");

float[] queryEmbedding = await model.EmbedQueryAsync("what is the capital of France?");
float[] passageEmbedding = await model.EmbedPassageAsync("Paris is the capital of France.");

Batch and Matryoshka-truncated (dimensions:) overloads exist for both, mirroring EmbedAsync.

Available Models

Four standard aliases (LocalEmbedder.LoadAsync("default"), etc.) plus a longer list of models loadable by their explicit short name (LocalEmbedder.LoadAsync("multilingual-e5-base")).

Aliases

Explicit models (by short name)

"Prefix" marks models fine-tuned with the query/passage convention (see Query/Passage Embeddings above) — — means the model needs no prefix and EmbedQueryAsync/EmbedPassageAsync behave as a plain passthrough for it.

GPU Acceleration

# NVIDIA GPU
dotnet add package Microsoft.ML.OnnxRuntime.Gpu

# Windows (AMD/Intel/NVIDIA)
dotnet add package Microsoft.ML.OnnxRuntime.DirectML